"""
数据处理路由模块
实现数据预处理、降维分析等接口
"""

# 导入FastAPI相关组件
from fastapi import APIRouter, HTTPException, status, BackgroundTasks
# 导入Pydantic模型用于数据验证
from pydantic import BaseModel
# 导入类型提示
from typing import List, Optional, Dict, Any, Union
# 导入数据处理库
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.isotonic import IsotonicRegression
from sklearn.calibration import CalibratedClassifierCV
import umap
# 导入文件操作库
import os
import json
import uuid
from datetime import datetime

# 导入Celery应用
try:
    from celery_app import celery_app
    CELERY_AVAILABLE = True
except ImportError:
    CELERY_AVAILABLE = False
    celery_app = None

# 创建API路由实例
router = APIRouter(
    prefix="/api/v1/data",  # 路由前缀
    tags=["data"],  # 标签
    responses={404: {"description": "Not found"}},  # 响应定义
)

# 数据模型定义
class DataProcessRequest(BaseModel):
    """
    数据处理请求数据模型
    """
    data: List[Dict[str, Any]]  # 数据
    operations: List[str]       # 操作列表

class DataProcessResponse(BaseModel):
    """
    数据处理响应数据模型
    """
    message: str                          # 响应消息
    processed_data: Optional[List[Dict[str, Any]]] = None  # 处理后的数据

class DimensionReductionRequest(BaseModel):
    """
    降维分析请求数据模型
    """
    data: List[Dict[str, Any]]  # 数据
    method: str                 # 降维方法（"pca", "tsne", "umap"）
    n_components: int           # 组件数量
    parameters: Optional[Dict[str, Any]] = None  # 额外参数

class DimensionReductionResponse(BaseModel):
    """
    降维分析响应数据模型
    """
    message: str                          # 响应消息
    reduced_data: Optional[List[Dict[str, Any]]] = None  # 降维后的数据
    explained_variance_ratio: Optional[List[float]] = None  # 解释方差比（仅PCA）

class CalibrationRequest(BaseModel):
    """
    模型校准请求数据模型
    """
    predictions: List[float]    # 预测值
    targets: List[float]        # 真实值
    method: str                 # 校准方法

class CalibrationResponse(BaseModel):
    """
    模型校准响应数据模型
    """
    message: str                          # 响应消息
    calibrated_predictions: Optional[List[float]] = None  # 校准后的预测值

class ExportRequest(BaseModel):
    """
    结果导出请求数据模型
    """
    data: List[Dict[str, Any]]  # 数据
    format: str                 # 导出格式（"csv", "json", "excel"）
    filename: str               # 文件名

class ExportResponse(BaseModel):
    """
    结果导出响应数据模型
    """
    message: str       # 响应消息
    file_path: str     # 文件路径

class AsyncTaskResponse(BaseModel):
    """
    异步任务响应数据模型
    """
    message: str       # 响应消息
    task_id: str       # 任务ID
    status: str        # 任务状态

# 异步任务状态查询响应模型
class TaskStatusResponse(BaseModel):
    """
    任务状态查询响应数据模型
    """
    task_id: str                # 任务ID
    status: str                 # 任务状态
    result: Optional[Any] = None # 任务结果

# 数据处理服务类
class DataProcessingService:
    """
    数据处理服务类
    提供各种数据处理功能的实现
    """
    
    @staticmethod
    def handle_missing_values(df: pd.DataFrame, method: str = "drop") -> pd.DataFrame:
        """
        处理缺失值
        Args:
            df (pd.DataFrame): 输入数据框
            method (str): 处理方法 ("drop", "mean", "median", "mode")
        Returns:
            pd.DataFrame: 处理后的数据框
        """
        if method == "drop":
            return df.dropna()
        elif method == "mean":
            return df.fillna(df.mean())
        elif method == "median":
            return df.fillna(df.median())
        elif method == "mode":
            return df.fillna(df.mode().iloc[0])
        else:
            return df
    
    @staticmethod
    def normalize_data(df: pd.DataFrame, method: str = "standard") -> pd.DataFrame:
        """
        数据标准化/归一化
        Args:
            df (pd.DataFrame): 输入数据框
            method (str): 标准化方法 ("standard", "minmax")
        Returns:
            pd.DataFrame: 标准化后的数据框
        """
        if method == "standard":
            scaler = StandardScaler()
            scaled_data = scaler.fit_transform(df.select_dtypes(include=[np.number]))
            scaled_df = pd.DataFrame(scaled_data, columns=df.select_dtypes(include=[np.number]).columns)
        elif method == "minmax":
            scaler = MinMaxScaler()
            scaled_data = scaler.fit_transform(df.select_dtypes(include=[np.number]))
            scaled_df = pd.DataFrame(scaled_data, columns=df.select_dtypes(include=[np.number]).columns)
        else:
            return df
        
        # 保留非数值列
        non_numeric_cols = df.select_dtypes(exclude=[np.number]).columns
        if len(non_numeric_cols) > 0:
            scaled_df = pd.concat([scaled_df, df[non_numeric_cols].reset_index(drop=True)], axis=1)
        
        return scaled_df
    
    @staticmethod
    def reduce_dimensionality(data: List[Dict[str, Any]], method: str, n_components: int, parameters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """
        降维分析
        Args:
            data (List[Dict[str, Any]]): 输入数据
            method (str): 降维方法 ("pca", "tsne", "umap")
            n_components (int): 组件数量
            parameters (Optional[Dict[str, Any]]): 额外参数
        Returns:
            Dict[str, Any]: 降维结果
        """
        if parameters is None:
            parameters = {}
        
        # 转换为DataFrame
        df = pd.DataFrame(data)
        
        # 选择数值列
        numeric_df = df.select_dtypes(include=[np.number])
        if numeric_df.empty:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail="数据中没有数值列用于降维分析"
            )
        
        # 根据方法进行降维
        if method.lower() == "pca":
            pca = PCA(n_components=n_components)
            reduced_data = pca.fit_transform(numeric_df)
            explained_variance_ratio = pca.explained_variance_ratio_.tolist()
            
            # 创建结果DataFrame
            columns = [f"PC{i+1}" for i in range(n_components)]
            result_df = pd.DataFrame(reduced_data, columns=columns)
            
            return {
                "reduced_data": result_df.to_dict(orient="records"),
                "explained_variance_ratio": explained_variance_ratio
            }
        
        elif method.lower() == "tsne":
            # t-SNE参数
            tsne_params = {
                "n_components": n_components,
                "random_state": parameters.get("random_state", 42),
                "perplexity": parameters.get("perplexity", 30.0),
                "n_iter": parameters.get("n_iter", 1000)
            }
            
            tsne = TSNE(**tsne_params)
            reduced_data = tsne.fit_transform(numeric_df)
            
            # 创建结果DataFrame
            columns = [f"TSNE{i+1}" for i in range(n_components)]
            result_df = pd.DataFrame(reduced_data, columns=columns)
            
            return {
                "reduced_data": result_df.to_dict(orient="records"),
                "explained_variance_ratio": None
            }
        
        elif method.lower() == "umap":
            # UMAP参数
            umap_params = {
                "n_components": n_components,
                "random_state": parameters.get("random_state", 42),
                "n_neighbors": parameters.get("n_neighbors", 15),
                "min_dist": parameters.get("min_dist", 0.1)
            }
            
            reducer = umap.UMAP(**umap_params)
            reduced_data = reducer.fit_transform(numeric_df)
            
            # 创建结果DataFrame
            columns = [f"UMAP{i+1}" for i in range(n_components)]
            result_df = pd.DataFrame(reduced_data, columns=columns)
            
            return {
                "reduced_data": result_df.to_dict(orient="records"),
                "explained_variance_ratio": None
            }
        
        else:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=f"不支持的降维方法: {method}"
            )
    
    @staticmethod
    def calibrate_model(predictions: List[float], targets: List[float], method: str) -> List[float]:
        """
        模型校准
        Args:
            predictions (List[float]): 预测值
            targets (List[float]): 真实值
            method (str): 校准方法 ("platt_scaling", "isotonic_regression")
        Returns:
            List[float]: 校准后的预测值
        """
        predictions = np.array(predictions)
        targets = np.array(targets)
        
        if method == "platt_scaling":
            # 使用简单的Platt缩放实现
            # 在实际应用中，应该使用sklearn的CalibratedClassifierCV
            # 这里简化实现
            try:
                # 确保预测值在(0,1)范围内
                predictions = np.clip(predictions, 1e-15, 1 - 1e-15)
                # 计算对数几率
                logits = np.log(predictions / (1 - predictions))
                # 简单线性回归拟合
                from sklearn.linear_model import LinearRegression
                model = LinearRegression()
                model.fit(logits.reshape(-1, 1), targets)
                # 校准预测
                calibrated_logits = model.predict(logits.reshape(-1, 1))
                calibrated_predictions = 1 / (1 + np.exp(-calibrated_logits))
                return calibrated_predictions.tolist()
            except Exception as e:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=f"Platt缩放校准失败: {str(e)}"
                )
        
        elif method == "isotonic_regression":
            try:
                # 使用sklearn的IsotonicRegression
                model = IsotonicRegression(out_of_bounds='clip')
                calibrated_predictions = model.fit_transform(predictions, targets)
                return calibrated_predictions.tolist()
            except Exception as e:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=f"Isotonic回归校准失败: {str(e)}"
                )
        
        else:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=f"不支持的校准方法: {method}"
            )
    
    @staticmethod
    def export_data(data: List[Dict[str, Any]], format: str, filename: str) -> str:
        """
        导出数据到指定格式的文件
        Args:
            data (List[Dict[str, Any]]): 要导出的数据
            format (str): 导出格式 ("csv", "json", "excel")
            filename (str): 文件名
        Returns:
            str: 文件路径
        """
        try:
            # 确保导出目录存在
            export_dir = "./exports"
            os.makedirs(export_dir, exist_ok=True)
            
            # 生成文件路径
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            unique_filename = f"{timestamp}_{filename}"
            file_path = os.path.join(export_dir, unique_filename)
            
            # 转换为DataFrame
            df = pd.DataFrame(data)
            
            # 根据格式导出
            if format.lower() == "csv":
                df.to_csv(file_path, index=False)
            elif format.lower() == "json":
                df.to_json(file_path, orient="records")
            elif format.lower() == "excel":
                df.to_excel(file_path, index=False)
            else:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=f"不支持的导出格式: {format}"
                )
            
            return file_path
        except Exception as e:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=f"导出失败: {str(e)}"
            )

# 异步任务定义
if CELERY_AVAILABLE:
    @celery_app.task(bind=True)
    def async_dimension_reduction(self, data: List[Dict[str, Any]], method: str, n_components: int, parameters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """
        异步执行降维分析任务
        Args:
            data (List[Dict[str, Any]]): 输入数据
            method (str): 降维方法
            n_components (int): 组件数量
            parameters (Optional[Dict[str, Any]]): 额外参数
        Returns:
            Dict[str, Any]: 降维结果
        """
        try:
            result = DataProcessingService.reduce_dimensionality(data, method, n_components, parameters)
            return result
        except Exception as e:
            self.update_state(state='FAILURE', meta={'error': str(e)})
            raise
    
    @celery_app.task(bind=True)
    def async_model_calibration(self, self_task, predictions: List[float], targets: List[float], method: str) -> List[float]:
        """
        异步执行模型校准任务
        Args:
            predictions (List[float]): 预测值
            targets (List[float]): 真实值
            method (str): 校准方法
        Returns:
            List[float]: 校准后的预测值
        """
        try:
            result = DataProcessingService.calibrate_model(predictions, targets, method)
            return result
        except Exception as e:
            self_task.update_state(state='FAILURE', meta={'error': str(e)})
            raise
    
    @celery_app.task(bind=True)
    def async_export_data(self, self_task, data: List[Dict[str, Any]], format: str, filename: str) -> str:
        """
        异步执行数据导出任务
        Args:
            data (List[Dict[str, Any]]): 要导出的数据
            format (str): 导出格式
            filename (str): 文件名
        Returns:
            str: 文件路径
        """
        try:
            file_path = DataProcessingService.export_data(data, format, filename)
            return file_path
        except Exception as e:
            self_task.update_state(state='FAILURE', meta={'error': str(e)})
            raise

# 路由定义
@router.post("/process", response_model=DataProcessResponse)
async def process_data(request: DataProcessRequest):
    """
    数据预处理接口
    实现数据预处理逻辑，如缺失值处理、标准化等
    Args:
        request (DataProcessRequest): 数据处理请求
    Returns:
        DataProcessResponse: 数据处理响应
    """
    try:
        # 转换为DataFrame
        df = pd.DataFrame(request.data)
        
        # 应用操作
        for operation in request.operations:
            if operation.startswith("missing:"):
                method = operation.split(":")[1]
                df = DataProcessingService.handle_missing_values(df, method)
            elif operation.startswith("normalize:"):
                method = operation.split(":")[1]
                df = DataProcessingService.normalize_data(df, method)
        
        return {
            "message": "数据预处理完成",
            "processed_data": df.to_dict(orient="records")
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"数据处理失败: {str(e)}"
        )

@router.post("/reduce-dimension", response_model=DimensionReductionResponse)
async def reduce_dimension(request: DimensionReductionRequest):
    """
    降维分析接口
    实现降维分析逻辑，如PCA、t-SNE、UMAP等
    Args:
        request (DimensionReductionRequest): 降维分析请求
    Returns:
        DimensionReductionResponse: 降维分析响应
    """
    try:
        # 执行降维分析
        result = DataProcessingService.reduce_dimensionality(
            request.data, 
            request.method, 
            request.n_components, 
            request.parameters
        )
        
        return {
            "message": f"使用{request.method.upper()}方法完成降维分析",
            "reduced_data": result["reduced_data"],
            "explained_variance_ratio": result["explained_variance_ratio"]
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"降维分析失败: {str(e)}"
        )

@router.post("/reduce-dimension-async", response_model=AsyncTaskResponse)
async def reduce_dimension_async(request: DimensionReductionRequest):
    """
    异步降维分析接口
    实现异步降维分析逻辑，如PCA、t-SNE、UMAP等
    Args:
        request (DimensionReductionRequest): 降维分析请求
    Returns:
        AsyncTaskResponse: 异步任务响应
    """
    if not CELERY_AVAILABLE:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="Celery未配置，无法执行异步任务"
        )
    
    try:
        # 提交异步任务
        task = async_dimension_reduction.delay(
            request.data,
            request.method,
            request.n_components,
            request.parameters
        )
        
        return {
            "message": "降维分析任务已提交",
            "task_id": task.id,
            "status": "PENDING"
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"提交降维分析任务失败: {str(e)}"
        )

@router.post("/calibrate", response_model=CalibrationResponse)
async def calibrate_model(request: CalibrationRequest):
    """
    模型校准接口
    实现模型校准逻辑
    Args:
        request (CalibrationRequest): 模型校准请求
    Returns:
        CalibrationResponse: 模型校准响应
    """
    try:
        # 执行模型校准
        calibrated_predictions = DataProcessingService.calibrate_model(
            request.predictions,
            request.targets,
            request.method
        )
        
        return {
            "message": "模型校准完成",
            "calibrated_predictions": calibrated_predictions
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"模型校准失败: {str(e)}"
        )

@router.post("/calibrate-async", response_model=AsyncTaskResponse)
async def calibrate_model_async(request: CalibrationRequest):
    """
    异步模型校准接口
    实现异步模型校准逻辑
    Args:
        request (CalibrationRequest): 模型校准请求
    Returns:
        AsyncTaskResponse: 异步任务响应
    """
    if not CELERY_AVAILABLE:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="Celery未配置，无法执行异步任务"
        )
    
    try:
        # 提交异步任务
        task = async_model_calibration.delay(
            request.predictions,
            request.targets,
            request.method
        )
        
        return {
            "message": "模型校准任务已提交",
            "task_id": task.id,
            "status": "PENDING"
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"提交模型校准任务失败: {str(e)}"
        )

@router.post("/export", response_model=ExportResponse)
async def export_results(request: ExportRequest):
    """
    结果导出接口
    实现结果导出逻辑
    Args:
        request (ExportRequest): 结果导出请求
    Returns:
        ExportResponse: 结果导出响应
    """
    try:
        # 执行结果导出
        file_path = DataProcessingService.export_data(
            request.data,
            request.format,
            request.filename
        )
        
        return {
            "message": f"结果已导出为{request.format.upper()}格式",
            "file_path": file_path
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"结果导出失败: {str(e)}"
        )

@router.post("/export-async", response_model=AsyncTaskResponse)
async def export_results_async(request: ExportRequest):
    """
    异步结果导出接口
    实现异步结果导出逻辑
    Args:
        request (ExportRequest): 结果导出请求
    Returns:
        AsyncTaskResponse: 异步任务响应
    """
    if not CELERY_AVAILABLE:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="Celery未配置，无法执行异步任务"
        )
    
    try:
        # 提交异步任务
        task = async_export_data.delay(
            request.data,
            request.format,
            request.filename
        )
        
        return {
            "message": "结果导出任务已提交",
            "task_id": task.id,
            "status": "PENDING"
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"提交结果导出任务失败: {str(e)}"
        )

@router.get("/task/{task_id}", response_model=TaskStatusResponse)
async def get_task_status(task_id: str):
    """
    获取异步任务状态接口
    查询指定任务的执行状态和结果
    Args:
        task_id (str): 任务ID
    Returns:
        TaskStatusResponse: 任务状态响应
    """
    if not CELERY_AVAILABLE:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="Celery未配置，无法查询任务状态"
        )
    
    try:
        # 获取任务状态
        task = celery_app.AsyncResult(task_id)
        
        return {
            "task_id": task_id,
            "status": task.status,
            "result": task.result if task.ready() else None
        }
    except Exception as e:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"查询任务状态失败: {str(e)}"
        )